Identification and localization of anomalous crop health patterns

ABSTRACT

A method and system for generating a map identifying the size and location of anomalous crop health patterns of a geographic area. Predictive crop health forecasting based historical crop health images generates expected crop health images. Statistical parametric mapping is used to model differences in the expected crop health images and current crop health images to generate a statistical parametric map. Regions of anomalous crop health based on the modeled differences are identified in the statistical parametric map. The number of the identified anomalous crop health regions and the size of each of the identified anomalous crop health regions are determined. The statistical significance of the size and number of the anomalous crop health regions relative to the expected crop health is quantified. A map of anomalous crop health patterns delineates the anomalous crop health regions and the statistical significance of the size and number of anomalous crop health regions.

BACKGROUND OF THE INVENTION

This disclosure is directed to computers, and computer applications foridentifying crop health, and more particularly to computer-implementedmethods and systems for generating a map identifying the size andlocation of anomalous crop health patterns of a geographic area.

Acquisition of geo-registered airborne imagery from various platformswith the goal of assessing crop health and/or crop growth or yield isknown. There are several known approaches and tools developed to acquireand analyze crop health. However, the known techniques use some form ofan empirical approach to utilize the results of the analysis todetermine where crop health/growth is degraded and where it is not. Somecurrent systems are focused on integrating the data, displaying theimagery and letting the determination of areas of anomalous crop health,whether good or bad, to be made by hand using the farmer's experienceand expert judgment.

Examples of such current systems are Decision Support System forAgrotechnology Transfer (DSSAT) and Daisy. The crop simulation models inDSSAT simulate growth, development and yield as a function of thesoil-plant-atmosphere dynamics. Daisy is a soil-plant-atmosphere systemmodel designed to simulate water balance, heat balance, solute balanceand crop production in agro-ecosystems subjected to various managementstrategies. DSSAT and Daisy employ mechanistic models and algorithms toforecast crop growth, yields and crop health.

Some current systems use color range in the imagery to present aqualitative assessment of anomalous crop health, which can often bemisleading. There is no way in the current systems to separate what arereal anomalies from regions of high/low crop health/growth that arestill within expected or normal bounds. In addition, the current systemslack predictive modeling to understand expected behavior at times in thefuture

Some known vegetation classification approaches rely on manualidentification of a reference location or plot of healthy vegetation. Inone known image analysis system, red appears to indicate areas of poorcrop health and green is acceptable, but typically there is no colorscale defining the values on the image and the actual difference betweenred and green is unknown.

SUMMARY OF THE INVENTION

One embodiment of a computer implemented method for generating a mapidentifying the size and location of anomalous crop health patterns of ageographic area includes storing historical crop health images of ageographic area in a computer data base and forecasting the expectedcrop health of regions within the geographic area based on thehistorical crop health images. In one embodiment, a forecastingalgorithm is used to predict the expected crop health of regions. Theforecasting is done with a predictive crop health forecasting computermodeling module to generate expected crop health images. The computerimplemented method further includes obtaining current crop health imagesof the geographic area and using a statistical parametric mappingcomputer module to generate a statistical parametric map that definesdifferences in the expected crop health images and the current observedcrop health images. Regions of anomalous crop health based on themodeled differences are identified in the statistical parametric map. Inaddition, computer implemented method includes determining the number ofthe identified anomalous crop health regions, determining the size ofeach of the identified anomalous crop health regions and quantifying thestatistical significance of the size and number of the anomalous crophealth regions relative to the expected crop health using thestatistical parametric map. A geographic area map is generated ofanomalous crop health patterns in which the map delineates the anomalouscrop health regions and the statistical significance of the size andnumber of the anomalous crop health regions.

A system that includes one or more processors operable to perform one ormore methods described herein also may be provided.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram of one embodiment of the method for identifyinganomalous crop health regions disclosed in this specification.

FIG. 2 is a flow diagram of one embodiment of a statistical parametricmapping method disclosed in this specification.

FIG. 3 an illustrative diagram of one embodiment of the system foridentifying anomalous crop health regions disclosed in thisspecification.

FIG. 4 is a graph of weekly NDVI observations for three locations incentral USA over 3 years.

FIG. 5 is an illustration of one example of the application of themethod and system disclosed in the specification.

FIG. 6 is a block diagram of an exemplary computing system suitable forimplementation of the embodiments disclosed in this specification.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Crop health/growth forecasting is uncertain and dependent on a number ofpoorly measured variables. Crop health/growth is not uniform andvariations in crop health within the scale of a single field makeidentification of significant variations difficult. The present methodand system makes use of historical crop health imagery and new sensingplatforms. The method and system eliminates, or minimizes, the need forfield-based surveys to sample crops or identify reference regions ofgood crop health.

Variations in soil characteristics, slope, elevation, seeds, fertilizerand pesticide application, infestation, irrigation, soil moistureretention, etc. create expected variation in crop health across a field.The present method and system, in one embodiment, integrates historicaland current imagery and accounts for expected spatial variation in crophealth to identify regions of anomalous crop health/growth. The presentmethod and system compares a crop health forecast to current crophealth, identifies the locations of regions of degraded crop health,assigns significance to those regions and alerts the interested partiesincluding providing a map of those locations.

The present method and system is an improvement over prior systems bydetermining what is a signal and what is noise and to identifystatistically significant regions of degraded crop health. The presentmethod and system couples tools for imaging of crop health withpredictive models (physical, statistical, artificial intelligence) ofcrop health/growth. The present method and system utilizes images andmodels in a way that enables separation of signal from noise in theimages to determine areas of concern and to assign a significance tothose areas.

In one embodiment, present method and system identifies areas ofanomalous crop health or growth from satellite, airborne, drone (UAV) orground vehicle based imaging of crop health indices. In one embodiment,the present method and system provides locations of anomalous growth andthe statistical significance of those regions relative to expected cropgrowth. The present method and system provides increased value forexisting historical crop health imagery integrating it into forecasts offuture crop growth for comparison against observed crop growth. Thepresent method provides locations and timing of anomalous crop growthpatterns and the statistical significance for the locations, magnitude,size (spatial) and timing of the anomalous crop growth patterns toseparate signal from noise in images. The present method and systemprovides an information technology framework for the transmission andupdate of identified anomalies to the user and other interested parties.

The present method and system, in one embodiment, utilizes quantitativeforecasts of crop health/growth based on prior data and other exogenousdata (e.g. weather variables) through machine learning, statisticalforecasting and/or artificial intelligence algorithms. The presentmethod and system utilizes a measure of uncertainty in the quantitativeforecasts of crop growth. The present method and system utilizes bothhistorical and up-to-date imagery of crop health. The present method andsystem, in one embodiment, trains machine learning, statistical orartificial intelligence models to forecast crop health and quantifyuncertainty

Examples of statistical forecasting models include multiple regressionmodels, autoregressive models (ARIMA, SARIMA, ARAMX, ARMA) and othertime series and filtering approaches and combinations of any of theabove models. Examples of machine learning forecasting models includesupport vector regression model, and generalized additive models.Examples of artificial intelligence forecasting models include recurrentneural networks and convolutional neural networks or other deep learningapproaches.

The present method and system, in one embodiment, provides quantitativedifferences between forecasted and observed crop health, the statisticalsignificance of the differences between forecasted and observed crophealth and location and extent of the regions where crop health issignificantly different than expected. The present method and systemtreats the difference between expected and observed crop health as acontinuous spatial field and then thresholds that continuous field toidentify anomalous regions.

The present method and system, in one embodiment, provides a modelingplatform to integrate predictive model forecasts into the evaluation ofcrop health that relies on an existing database of crop health imagescovering multiple growing seasons. The present method and systemincludes a means of collecting geo-registered images of crop health fromsatellite, airborne, or ground-based platforms at a regular timeinterval and uniform resolution. The present method and system providesquantitative forecasts of crop health with a measure ofconfidence/uncertainty in the forecast by employing a predictive modelto estimate the expected crop health/growth conditions at any given timein the future. The present method and system, in one embodiment, employsa predictive model that is continuously updated with observational datato provide a custom, up-to-date, forecast of crop growth for thespecific time and location of the next observation. The predictive modelprovides an estimate of crop health/growth across the region of interestand quantifies the uncertainty in that forecast.

The present method and system, in one embodiment, employs statisticalparametric mapping to identify areas of degraded crop health and assignsignificance to those areas. Statistical parametric mapping refers tothe construction and assessment of spatially extended statisticalprocesses used to test hypotheses about functional imaging data. Thedifferences between forecast and observed crop health are defined in astatistical parametric map. In one embodiment, the statisticalparametric mapping determines the location, intensity and size ofanomalous regions. Inherent in the statistical parametric mappingapproach is the determination of the spatial correlation of thedifferences between the observed and expected crop health. An outcome ofthe statistical parametric mapping approach is that the chances ofobserving a given size of an identified anomalous region under normalcrop growth conditions, the significance, can be determined. Thissignificance is a function of both the size and the intensity of theanomalous region. Additionally, use of the statistical parametricmapping process enables the determination of the significance of thenumber of anomalous regions.

Measuring and forecasting crop growth is not an exact science.Variations in the weather, soil conditions, seed quality, pests,disease, irrigation, and other factors make comparison of any observedcrop growth against growth in previous time periods difficult. Thesevariations lead to uncertainty in what can be considered normal vs.anomalous growth. Additionally, there is noise inherent in themeasurements of crop growth from remote sensing imagery that alsocontributes to uncertainty in what is anomalous vs. normal. The presentmethod and system addresses these uncertainties, in aggregate, through astatistical model and calculates statistical significance to determinethe chance that any size, intensity or number of anomalies would occurunder background growth conditions where background conditions encompassall of the variations and uncertainties mentioned above.

Advantages of the present method and system over the current knownsystems include identifying specific zones of anomalous crop health andproviding geographic locations, and separating signal from noise usingstatistical parametric mapping. In addition, the present method andsystem does not require identification of a reference area of good crophealth through field investigation, but does allow for any species ofcrop and different types of risk metrics. Further, the present methodand system specifically calculates the probability, or statisticalsignificance, of one, or more, geographic regions, those above or belowa threshold, occurring under expected or normal health/growthconditions. The present method and system is different from anytechnique discussing significance at a pixel-wise (univariate) level.

The present method and system, in one embodiment, combines forecasthealth/growth as derived from one or more learning algorithms withobserved health/growth to identify the location and extent of anomalouscrop health and assigns a significance to that anomalous region. Thepresent method and system utilizes existing, historical images of crophealth and up-to-date (such as real time) images of crop health andengages a learning system to forecast expected crop health frominformation contained in existing images. The present method and system,in one embodiment, models differences in expected and imaged crop healthas a spatially correlation, multivariate random field that can include:F, t, χ and Z (Gaussian) random fields and uses properties of themultivariate random field as input to the statistical parametric mappingsystem. The present method and system identifies regions ofanomalous/degraded crop health, records the numbers and size of theseregions, quantifies the statistical significance of the size and numberof anomalous regions relative to expected crop health and delineatesregions of degraded crop health on a geographic map.

FIG. 1 is a flow chart of one embodiment of a computer implementedmethod for generating a map identifying the size and location ofanomalous crop health patterns of a geographic area. Step S101 storeshistorical crop health images of a geographic area in a computer database. Step S102 forecasts expected crop health of regions within thegeographic area based on the historical crop health images of thegeographic area using a predictive crop health forecasting computermodeling module. In one embodiment, the expected crop health forecast isalso based on related covariates (e.g. weather forecasts). Step S103generates expected crop health images. Step S104 obtains current crophealth images of the geographic area. Step S105 defines differences inthe expected crop health images and the current crop health images in astatistical parametric map using a statistical parametric mappingcomputer module. Step S106 identifies regions of anomalous crop healthbased on the modeled differences in the statistical parametric map. StepS107 determines the number of the identified anomalous crop healthregions. Step S108 determines the size of each of the identifiedanomalous crop health regions. Step S109 quantifies the statisticalsignificance of the size and number of the anomalous crop health regionsrelative to the expected crop health using the statistical parametricmap. Step S110 generates a map of anomalous crop health patterns of thegeographic area, the map delineating the anomalous crop health regionsand the statistical significance of the size and number of the anomalouscrop health regions.

In one embodiment of the method of FIG. 1, the predictive crop healthforecasting computer modeling module includes one of a machine learningmodel, a statistical model and an artificial intelligence model. Inaddition, in one embodiment, the machine learning model is one of asupport vector regression model, a random forest model and a generalizedadditive model. In one embodiment the statistical model is one of amultiple regression model, an auto-regressive model and a time seriesfiltering model. In one embodiment the artificial intelligence model isone of recurrent neural networks, convolutional neural networks or otherdeep neural network.

The statistical parametric mapping requires a measure of uncertainty inthe forecast and/or observed crop health. In one embodiment of themethod of FIG. 1, a time series forecasting algorithm produces aconfidence interval on the forecast crop health values. In oneembodiment, the statistical parametric mapping module utilizes anuncertainty estimate for quantifying the statistical significance of thesize and number of the anomalous crop health regions. In one embodiment,the predictive crop health forecasting computer modeling module includesa learning system to provide an uncertainty estimate in generating theexpected crop health images.

FIG. 2 is a flow chart of one embodiment of the modeling using thestatistical parametric mapping module. Step S111 starts the statisticalparametric mapping process. Step S112 conducts a univariate statisticaltest at all pixels of the expected crop health images and the currentcrop health images. Step S113 generates a set of test statistics. StepS114 performs spatial smoothing of the set of test statistics. Step S115generates a distribution transform of the smoothed set of teststatistics. Step S116 defines differences in expected and imaged crophealth as a spatially correlated, multivariate random field thatincludes F, t, χ and Z (Gaussian) random fields based on thedistribution transform. Step 117 generates the statistical parametricmap using properties of the multivariate random field and thetransformed distribution transform of the smoothed set of teststatistics.

FIG. 3 is a block diagram of one embodiment of a system for generating amap identifying the size and location of anomalous crop health patternsof a geographic area. Data acquisition module 10 obtains the imageryfrom various sources, such as drones, satellites, aircraft orground-based platforms. A geo-registration and ortho-rectification crophealth imagery module 12 transmits the output imagery to a database 14of historical imagery taken at times [1, . . . , t] and a database 16 ofcurrent images at time [t+1]. A spatial-temporal forecasting module 18uses statistical and machine learning algorithms that use the historicaldata to generate a crop health forecast 20 at time (t+1). Module 18 maybe incorporated in program module 120 of FIG. 5 described below.

The current image data from database 16 and the resulting forecast fromthe Forecast module (20) are input to statistical parametric mappingmodule 22. Statistical parametric mapping module 22 may be incorporatedin program module 120 of FIG. 5 described below. In statistical testmodule 24 a set of univariate statistical tests is conducted at everypixel of the forecast and currently observed images to provide atest-statistic at every pixel. In spatial smoothing module 26 thatspatial representation of test statistics is then smoothed. Distributiontransform module 28 generates a distribution transform of the set oftest statistics and the distribution re-inflated. In one alternativeembodiment, the re-inflation of the distribution is not done because allsubsequent calculations and results are relative to the standarddeviation of the distribution and therefore, the same process can bedone using the standard deviation of the distribution after thesmoothing. However, re-inflation is done because it is generally easierto work with, and understand, results when the final SPM has unit (1.0)standard deviation. Module 30 defines differences in the expected crophealth images and the current crop health images to generate astatistical parametric map. Module 32 quantifies the significance of thesize, number and/or amplitude of excursion sets and their locations.Communications module 34 generates a geographic area map of anomalouscrop health patterns. The map delineating the anomalous crop healthregions and the statistical significance of the size and number of theanomalous crop health regions. The map is communicated to the user andother interested persons or entities.

One example of an application of the present method and system is insoil moisture deficit. Soil moisture deficit leads to decreased plantgrowth. Soil moisture deficit may be a localized phenomena such as in anirrigated field where irrigation problems impact a portion of the fieldor it may be widespread where the entire crop growing region is impactedby the moisture deficit as is more commonly exhibited in a non-irrigatedgrowing region. The present method and system is capable of detectingeither expression of soil moisture deficit and differentiating betweenthe two.

Another example of an application of the present method and system is innitrogen deficit. Readily available nitrogen in the soil is necessaryfor plant growth. In areas with limited nitrogen supply, the chlorophylllevel of the plants is reduced and this decrease in chlorophyll isdetectable by airborne imagery and apparent in the normalized differencevegetation index (NDVI) that is derived from this imagery. The presentmethod and system can compare NDVI derived from the current imagery andcompare that to expected NDVI values and identify regions of significantdeviation from expected values.

Another example of an application of the present method and system is inpestilence. A wide array of pests can alter plant growth. The types ofpestilence and the mechanisms with which they alter plant growth varyacross the combinations of pests and the plants. These mechanisms rangefrom creating mutations at the sub-cellular level to parasitic growth ofanother plant onto the crop. However, because all of these expressionsof pestilence alter the crop health by decreasing green chlorophyll,they are detectable through airborne imaging. Significant deviationsfrom expected crop health expressed within the imagery can be detectedand localized with the present method and system.

FIGS. 4 and 5 show an illustrative example of how statistical parametricmapping is applied to identify regions of anomalous crop growth for NDVImeasures of the Midwest, USA. NDVI measures absorbance of light in thevisual spectrum by chlorophyll and scattering of light in thenear-infrared by other plant material and is used as general measure ofvegetation health.

The graph of FIG. 4 shows weekly NDVI observations for three locationsin central US over 3 years. The satellite images available for analysisin the Midwest US have a 16×16 km pixel resolution. The coarseresolution aggregates multiple crop types and non-crop land area into asingle pixel. Ideal conditions are image resolution of meters to 10's ofmeters and a single crop. This example calculation is done at a muchcoarser scale for illustrative purposes only.

A simple linear filter is used as the example predictive model. Thisfilter is applied as a time series forecast at every pixel and isindependent of neighboring pixels. Other time-series forecasting modelscan be fit to these historical data. The use of more sophisticatedtime-series forecasts can be employed in the present method and system.

The distribution of forecast errors is assumed Gaussian and used toderive the pixel-wise standard deviation used in the statistical test.In this example, a t-test is used as the pixel-wise (univariate) test.The images are available weekly and the forecast is shown for a singleweek. The sequence of maps generated in the example SPM process areshown in the FIG. 5. Map 40 shows estimated NDVI obtained from thepredictive forecasting model. Map 41 is the NDVI map observed fromcurrent imagery. Map 43 shows the raw differences between the estimatedand observed NDVI locations. Map 44 is the standard deviation of thosedifferences from the estimated NDVI location. The data from maps 43 and44 are input to the univariate t-test module 45 which employs theequation:

$t = \frac{\overset{\_}{x} - \mu_{0}}{s/\sqrt{n}}$

Module 46 performs spatial smoothing and generates a distributiontransform (t to Z).

Map 47 shows the resulting statistical parametric map.

Map 47 has a spatial correlation length (Full Width at Half-Maximum) of11.4 pixels in the east-west direction and 8.9 pixels in thenorth-south. SPM is a multivariate Gaussian field and color legend isstandard deviations from the zero-mean SPM is truncated at a thresholdof +/−2.5. Excursions above the threshold are shown in map 48 andexcursions below the threshold are shown in map 49. Several regions ofpotential poor crop health are identified as excursions outside thethreshold. The largest excursions are examined for significance.

The statistical parametric map calculation is shown below. The levelinference for excursion regions is set.

${P_{w}\left( {u,k,c} \right)} \approx {1 - {\sum\limits_{i = 0}^{c - 1}{\sum\limits_{j = 1}^{\infty}{{P\left( {m = j} \right)} \cdot \begin{pmatrix}j \\i\end{pmatrix} \cdot {P\left( {n \geq k} \right)}^{i} \cdot {P\left( {n < k} \right)}^{j - 1}}}}}$

P(u,k,c)=Probability of getting>=c regions, each of size>=k pixels,above the threshold u in an area of size S from a multiGaussian fieldwith spatial correlation of W.

m=number of regions above a threshold u

n=number of pixels above a threshold u

E[m]=Expected value of number of regions (Euler characteristic)

=Poisson pdf

P(n≥k)

The largest positive excursion is 122 pixels. For a multivariateGaussian field with the calculated FWHM truncated at a threshold of 2.5,the probability of getting an excursion of this size (p-value) is 0.054(close, but not significant at an α=0.050 level). The largest negativeexcursion is 25 pixels. For a multivariate Gaussian field with thecalculated FWHM truncated at a threshold of −2.5, the probability ofgetting an excursion of this size (p-value) is 0.650 (not significant atan α=0.050 level). The conclusion reached on these data is that thereare regions of potential unexpected crop health, but they are notsignificantly different from forecast crop health.

FIG. 6 illustrates a schematic of an example computer or processingsystem that may implement the method for generating a map identifyingthe size and location of anomalous crop health patterns of a geographicarea comprising in one embodiment of the present disclosure. Thecomputer system is only one example of a suitable processing system andis not intended to suggest any limitation as to the scope of use orfunctionality of embodiments of the methodology described herein. Theprocessing system shown may be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with the processingsystem shown in FIG. 6 may include, but are not limited to, personalcomputer systems, server computer systems, thin clients, thick clients,handheld or laptop devices, multiprocessor systems, microprocessor-basedsystems, set top boxes, programmable consumer electronics, network PCs,minicomputer systems, mainframe computer systems, and distributed cloudcomputing environments that include any of the above systems or devices,and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 100, a system memory 106, anda bus 104 that couples various system components including system memory106 to processor 100. The processor 100 may include a program module 102that performs the methods described herein. The module 102 may beprogrammed into the integrated circuits of the processor 100, or loadedfrom memory 106, storage device 108, or network 114 or combinationsthereof.

Bus 104 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 106 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 108 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 104 by one or more data media interfaces.

Computer system may also communicate with one or more external devices116 such as a keyboard, a pointing device, a display 118, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 110.

Still yet, computer system can communicate with one or more networks 114such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 112. Asdepicted, network adapter 112 communicates with the other components ofcomputer system via bus 104. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a non-transitory computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

In addition, while preferred embodiments of the present invention havebeen described using specific terms, such description is forillustrative purposes only, and it is to be understood that changes andvariations may be made without departing from the spirit or scope of thefollowing claims.

What is claimed is:
 1. A computer implemented method for generating amap identifying the size and location of anomalous crop health patternsof a geographic area, comprising: storing historical crop health imagesof a geographic area in a computer data base; forecasting expected crophealth of regions within the geographic area based on the historicalcrop health images of the geographic area using a predictive crop healthforecasting computer modeling module to generate expected crop healthimages; obtaining current crop health images of the geographic area;defining differences in the expected crop health images and the currentobserved crop health images in a statistical parametric map using astatistical parametric mapping computer module; identifying regions ofanomalous crop health based on the modeled differences in thestatistical parametric map; determining the number of the identifiedanomalous crop health regions; determining the size of each of theidentified anomalous crop health regions; quantifying statisticalsignificance of the size and number of the anomalous crop health regionsrelative to the expected crop health using the statistical parametricmap; and generating a geographic area map of anomalous crop healthpatterns, the map delineating the anomalous crop health regions and thestatistical significance of the size and number of the anomalous crophealth regions.
 2. The computer implemented method of claim 1, whereinthe predictive crop health forecasting computer modeling module includesone of a machine learning model, a statistical model and an artificialintelligence model.
 3. The computer implemented method of claim 2,wherein the machine learning model is one of a support vector regressionmodel, random forest model and a generalized additive model, wherein thestatistical model is one of a multiple regression model, anauto-regressive model and a time series filtering model, and wherein theartificial intelligence model is one of recurrent neural networks,convolutional neural networks or other deep learning approaches.
 4. Thecomputer implemented method of claim 1, wherein the statisticalparametric mapping module utilizes an uncertainty estimate forquantifying the statistical significance of the size and number of theanomalous crop health regions.
 5. The computer implemented method ofclaim 1, wherein the predictive crop health forecasting computermodeling module includes a learning system to provide an uncertaintyestimate in generating the expected crop health images.
 6. The computerimplemented method of claim 1, wherein the statistical parametricmapping module determines spatial correlation differences between theobserved crop health images and the expected crop health images.
 7. Acomputer system for generating a map identifying the size and locationof anomalous crop health patterns of a geographic area, comprising: oneor more computer processors; one or more non-transitorycomputer-readable storage media; program instructions, stored on the oneor more non-transitory computer-readable storage media, which whenimplemented by the one or more processors, cause the computer system toperform the steps of: storing historical crop health images of ageographic area in a computer data base; forecasting expected crophealth of regions within the geographic area based on the historicalcrop health images of the geographic area using a predictive crop healthforecasting computer modeling module to generate expected crop healthimages; obtaining current crop health images of the geographic area;defining differences in the expected crop health images and the currentobserved crop health images in a statistical parametric map using astatistical parametric mapping computer module; identifying regions ofanomalous crop health based on the modeled differences in thestatistical parametric map; determining the number of the identifiedanomalous crop health regions; determining the size of each of theidentified anomalous crop health regions; quantifying statisticalsignificance of the size and number of the anomalous crop health regionsrelative to the expected crop health using the statistical parametricmap; and generating a geographic area map of anomalous crop healthpatterns, the map delineating the anomalous crop health regions and thestatistical significance of the size and number of the anomalous crophealth regions.
 8. The computer system of claim 7, wherein thepredictive crop health forecasting computer modeling module includes oneof a machine learning model, a statistical model and an artificialintelligence model.
 9. The computer system of claim 8, wherein themachine learning model is one of a support vector regression model, arandom forest model and a generalized additive model, wherein thestatistical model is one of a multiple regression model, anauto-regressive model and a time series filtering model, and wherein theartificial intelligence model is one of recurrent neural networks,convolutional neural networks or other deep learning approaches.
 10. Thecomputer system of claim 7, wherein the statistical parametric mappingmodule utilizes an uncertainty estimate for quantifying the statisticalsignificance of the size and number of the anomalous crop healthregions.
 11. The computer system of claim 7, wherein the predictive crophealth forecasting computer modeling module includes a learning systemto provide an uncertainty estimate in generating the expected crophealth images.
 12. The computer system of claim 7, wherein thestatistical parametric mapping module determines spatial correlationdifferences between the observed crop health images and the expectedcrop health images.
 13. A computer program product comprising: programinstructions on a computer-readable storage medium, where execution ofthe program instructions using a computer causes the computer to performa method for generating a map identifying the size and location ofanomalous crop health patterns of a geographic area, comprising: storinghistorical crop health images of a geographic area in a computer database; forecasting expected crop health of regions within the geographicarea based on the historical crop health images of the geographic areausing a predictive crop health forecasting computer modeling module togenerate expected crop health images; obtaining current crop healthimages of the geographic area; defining differences in the expected crophealth images and the current observed crop health images in astatistical parametric map using a statistical parametric mappingcomputer module; identifying regions of anomalous crop health based onthe modeled differences in the statistical parametric map; determiningthe number of the identified anomalous crop health regions; determiningthe size of each of the identified anomalous crop health regions;quantifying statistical significance of the size and number of theanomalous crop health regions relative to the expected crop health usingthe statistical parametric map; and generating a geographic area map ofanomalous crop health patterns, the map delineating the anomalous crophealth regions and the statistical significance of the size and numberof the anomalous crop health regions.
 14. The computer program productof claim 13, wherein the predictive crop health forecasting computermodeling module includes one of a machine learning model, a statisticalmodel and an artificial intelligence model.
 15. The computer programproduct of claim 14, wherein the machine learning model is one of asupport vector regression model, a random forest model and a generalizedadditive model, wherein the statistical model is one of a multipleregression model, an auto-regressive model and a time series filteringmodel, and wherein the artificial intelligence model is one of recurrentneural networks, convolutional neural networks or other deep learningapproaches.
 16. The computer program product of claim 13, wherein thestatistical parametric mapping module utilizes an uncertainty estimatefor quantifying the statistical significance of the size and number ofthe anomalous crop health regions.
 17. The computer program product ofclaim 13, wherein the predictive crop health forecasting computermodeling module includes a learning system to provide an uncertaintyestimate in generating the expected crop health images.
 18. The computerprogram product of claim 13, wherein the statistical parametric mappingmodule determines spatial correlation differences between the observedcrop health images and the expected crop health images.
 19. The computerimplemented method of claim 6, wherein the statistical parametricmapping module uses spatially extended statistical processes to testhypotheses about functional imaging data.
 20. The computer system ofclaim 12, wherein the statistical parametric mapping module usesspatially extended statistical processes to test hypotheses aboutfunctional imaging data.